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StatsForecastAutoTheta

StatsForecastAutoTheta

class StatsForecastAutoTheta(season_length: int = 1, decomposition_type: str = 'multiplicative', model: str | None = None)[source]

Statsforecast AutoTheta estimator.

Direct interface to statsforecast.models.AutoTheta by Nixtla.

This estimator directly interfaces AutoTheta, from statsforecast [1] by Nixtla.

AutoTheta model automatically selects the best Theta (Standard Theta Model (“STM”), Optimized Theta Model (“OTM”), Dynamic Standard Theta Model (“DSTM”), Dynamic Optimized Theta Model (“DOTM”)) model using mse.

Parameters:
season_lengthint, optional, default=1

number of observations per unit of time (e.g. 24 for hourly data), by default 1

decomposition_typestr, optional, default=”multiplicative”

possible values: “additive”, “multiplicative” type of seasonal decomposition, by default “multiplicative”

modelOptional[str], optional

controlling Theta Model, by default searches the best model possible values: “STM”, “OTM”, “DSTM”, “DOTM”

Attributes:
cutoff

Cut-off = “present time” state of forecaster.

fh

Forecasting horizon that was passed.

is_fitted

Whether fit has been called.

state

State of the estimator.

See also

ThetaForecaster

References

Examples

>>> from sktime.datasets import load_airline
>>> from sktime.forecasting.statsforecast import StatsForecastAutoTheta
>>> y = load_airline()
>>> forecaster = StatsForecastAutoTheta(
...     season_length=12, decomposition_type="additive", model="OTM"
... )
>>> forecaster.fit(y)
StatsForecastAutoTheta(...)
>>> y_pred = forecaster.predict(fh=[1, 2, 3])

Methods

check_is_fitted([method_name])

Check if the estimator has been fitted.

clone()

Obtain a clone of the object with same hyper-parameters and config.

clone_tags(estimator[, tag_names])

Clone tags from another object as dynamic override.

create_test_instance([parameter_set])

Construct an instance of the class, using first test parameter set.

create_test_instances_and_names([parameter_set])

Create list of all test instances and a list of names for them.

fit(y[, X, fh])

Fit forecaster to training data.

fit_predict(y[, X, fh, X_pred])

Fit and forecast time series at future horizon.

get_class_tag(tag_name[, tag_value_default])

Get class tag value from class, with tag level inheritance from parents.

get_class_tags()

Get class tags from class, with tag level inheritance from parent classes.

get_config()

Get config flags for self.

get_fitted_params([deep])

Get fitted parameters.

get_param_defaults()

Get object's parameter defaults.

get_param_names([sort])

Get object's parameter names.

get_params([deep])

Get a dict of parameters values for this object.

get_pretrained_params([deep])

Get pretrained parameters of this estimator.

get_tag(tag_name[, tag_value_default, ...])

Get tag value from instance, with tag level inheritance and overrides.

get_tags()

Get tags from instance, with tag level inheritance and overrides.

get_test_params([parameter_set])

Return testing parameter settings for the estimator.

is_composite()

Check if the object is composed of other BaseObjects.

load_from_path(serial)

Load object from file location.

load_from_serial(serial)

Load object from serialized memory container.

predict([fh, X])

Forecast time series at future horizon.

predict_interval([fh, X, coverage])

Compute/return prediction interval forecasts.

predict_proba([fh, X, marginal])

Compute/return fully probabilistic forecasts.

predict_quantiles([fh, X, alpha])

Compute/return quantile forecasts.

predict_residuals([y, X])

Return residuals of time series forecasts.

predict_var([fh, X, cov])

Compute/return variance forecasts.

pretrain(y[, X, fh])

Pre-train forecaster on panel (global) data.

reset()

Reset the object to a clean post-init state.

save([path, serialization_format])

Save serialized self to bytes-like object or to (.zip) file.

score(y[, X, fh])

Scores forecast against ground truth, using MAPE (non-symmetric).

set_config(**config_dict)

Set config flags to given values.

set_params(**params)

Set the parameters of this object.

set_random_state([random_state, deep, ...])

Set random_state pseudo-random seed parameters for self.

set_tags(**tag_dict)

Set instance level tag overrides to given values.

update(y[, X, update_params])

Update cutoff value and, optionally, fitted parameters.

update_predict(y[, cv, X, update_params, ...])

Make predictions and update model iteratively over the test set.

update_predict_single([y, fh, X, update_params])

Update model with new data and make forecasts.